Recently, Compressive Sensing (CS), also known as compressed sensing, compressive sampling or sparse sampling, has attracted a lot of attention in the areas of signal, image processing, and communication due to a breakthrough in sampling rate reduction (e.g. with respect to the Nyquist sampling rate) whether it is in time/frequency or spatial/frequency domain. The person skilled in the art of signal processing knows that CS takes advantage of a signal's sparseness or compressibility in some domain, allowing the entire signal to be determined from relatively few measurements. CS also opens a new horizon for collecting quality sensory data due to the combination of new developments in sensing arrays to enhance the response time while reducing the number of sampling measurements as allowed by CS. Fortunately, the quality of information obtained through compressive sensing and in spite of the reduction of sampling rate (below Nyquist rate or below spatial rate of coarse pixel resolution) can be as unique as the information in Nyquist rate or fine pixel resolution [see refs. 1 and 2, incorporated by reference in their entirety]. This can help dynamic object recognition implementations for effective real time processing with associated low cost hardware implementation (e.g. computational imaging) without compromising information manifold quality.
In one example, the Defense Advanced Research Projects Agency (DARPA) has identified the role of computational imaging to reduce the demand of Size, Weight and Power (SWaP) on the actual imaging hardware, without losing the quality of the actual knowledge gathered. Indeed, compressive sensing is one of such emerging and extremely powerful techniques to enable meeting DARPA's goal.
FIG. 1 shows a functional system building block for a computational imaging used in a complementary metal-oxide-semiconductor (CMOS) imager integrated chip (IC). In the prior art embodiment of FIG. 1, a non-overlapped sub-window (n×n) from an (N×N) sub-pixel active-pixel sensor (APS) array (block A) is compressed through a projection matrix (block B) to obtain a compressive image (block C). For example, an (n×n) sub-pixel array (e.g. corresponding to a sub-window (n×n)) is compressed to (m) values to obtain a (m×1) vector. The (m×1) vectors obtained by compressing all the (n×n) sub-pixel arrays (e.g. corresponding to all of the non-overlapped (n×n) sub-windows contained within the (N×N) sub-pixel array), are correlated between input image for each non-overlapped sub-window (n×n) and predetermined targets, to find the best match to a predetermined target in a database. The correlation level will indicate the potential match to a predetermined target and its location in the focal plane (N×N). The combination of recovery image, target types and location will be shown in block D (e.g. in the soldier goggle).
Current CMOS imagers, such as one depicted in FIG. 1 using an APS sensor, are low power and low cost, but are typically a noisier visual sensing approach [see ref. 3, incorporated herein by reference in its entirety] as compared to a CCD visual sensing approach. A CMOS imager with fast frame rate, high quality image and intelligent processing on chip can be attractive to certain tasks and/or systems, such as for example in the case of an unmanned aerial vehicle (UAV), to enable spatial, temporal, or functional capabilities required by an individual warfighter. However, the integration of high quality CMOS imager and intelligent on-chip processing has been done [see ref. 4 incorporated herein by reference in its entirety] and has faced the challenges of speed, power, quality, detection accuracy and tracking performance and was thus unable to meet the requirements of for example an autonomous miniaturized UAV as defined for example by DARPA.